Roy Ben-Shalom1, Alexander Ladd2, Nikhil S Artherya2, Christopher Cross3, Kyung Geun Kim2, Hersh Sanghevi2, Alon Korngreen4, Kristofer E Bouchard5, Kevin J Bender6. 1. Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, San Francisco, CA, United States; MIND Institute University of California, Davis, CA, United States; Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States. Electronic address: rbenshalom@ucdavis.edu. 2. Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States. 3. Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States. 4. The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel. 5. Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States; Hellen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States; Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, CA, United States. 6. Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, San Francisco, CA, United States. Electronic address: kevin.bender@ucsf.edu.
Abstract
BACKGROUND: The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW METHOD: Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING METHODS: NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. CONCLUSIONS: Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists. Published by Elsevier B.V.
BACKGROUND: The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW METHOD: Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING METHODS: NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. CONCLUSIONS: Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists. Published by Elsevier B.V.
Authors: Alexander Ladd; Kyung Geun Kim; Jan Balewski; Kristofer Bouchard; Roy Ben-Shalom Journal: Front Neuroinform Date: 2022-06-17 Impact factor: 3.739